Adversarial machine learning (AdvML), which aims at tricking ML models by providing deceptive inputs, has been identified as a powerful method to improve various trustworthiness metrics (e.g., adversarial robustness, explainability, and fairness) and to advance versatile ML paradigms (e.g., supervised and self-supervised learning, and static and continual learning). As a consequence of the proliferation of AdvML-inspired research works, the proposed workshop–New Frontiers in AdvML–aims to identify the challenges and limitations of current AdvML methods and explore new prospective and constructive views of AdvML across the full theory/algorithm/application stack. The workshop will explore the new frontiers of AdvML from the following new perspectives: (1) advances in foundational AdvML research, (2) principles and practice of scalable AdvML, and (3) AdvML for good. This will be a full-day workshop, which accepts full paper submissions (up to 6 pages) as well as “blue sky” extended abstract submissions (up to 2 pages).